Nr3D. Tab. REF  reports the grounding accuracy on Nr3D [1]} dataset. Our MVT greatly surpasses all previous methods. Compared with the best competitor [2]} using the same training setting, MVT outperforms the state-of-the-art method by \(+11.2\%\) , from \(43.9\%\)  to \(55.1\%\) . Even when compared with SAT [3]}, which utilizes extra 2D semantics assisted its training, our MVT still outperforms it by \(5.9\%\)  absolute value.
Specifically, the high accuracy on the “Easy” split (\(11.3\%\)  better than LanguageRefer [2]}) reflects the better visual-language alignment of our MVT. At the same time, the high accuracy on the “View-dep” split (\(12.6\%\)  better than LanguageRefer [2]}) reflects the effectiveness and robustness of the multi-view representation learned by our MVT. Tab. REF  also reports the performance of training on Nr3D together with Sr3D and Sr3D+ datasets [1]}. Both synthetic datasets expand the scale of the training set, therefore further improving the performance on Nr3D. As the results show, our MVT on “Nr3D w/ Sr3D” and “Nr3D w/ Sr3D+” achieves the overall grounding accuracy of \(58.5\%\)  and \(59.5\%\)  respectively, which significantly surpasses the state-of-the-art method [7]} by \(11.3\%\)  and \(11.5\%\) .
